Models of the Fish Yield from Lakes: Does the Random Component Matter?

Abstract

Generalized linear models were used to investigate the sensitivity of paramater estimates to choice of the random error assumption in models of fisheries data. We examined models of fish yield from lakes as a function of (i) Ryder's morphoedaphic index, (ii) lake area, lake depth, and concentration of dissolved solids, and (iii) fishing effort. Models were fit using a normal, log-normal, gamma, or Poisson distribution to generate the random error. Plots of standardized Pearson residuals and standardized deviance residuals were used to evaluate the distributional assumptions. For each data set, observations were found to be consistent with several distributions; however, some distributions were shown to be clearly inappropriate. Inappropriate distributional assumptions produced substantially different parameter estimates. Generalized linear models allow a variety of distributional assumptions to be incorporated in a model, and thereby let us study their effects.